Course Description:
- This course offers an in-depth exploration of Computer Vision, tracing its evolution from classical image processing to modern transformer-based and zero-shot vision models
- You will build a strong foundation in the mathematical and perceptual principles underlying vision systems and progressively master deep learning-driven methods for detection, segmentation, and tracking
- Through structured hands-on labs and domain-specific projects, you will acquire the ability to design, train, and deploy complete computer vision solutions, linking theoretical knowledge with industry-level implementation and future trends in AI vision technologies
Course main points:
- Vision in Context:
- Origins and Evolution
- Foundations of Image:
- Formation & Processing
- Deep Learning Foundations for Vision
- Object Detection and Localization
- Segmentation and Visual Understanding
- Tracking and Motion Analysis
- Vision Transformers and Data-Efficient Learning
- Integration, Deployment, and Future Directions
Duration:
44 Hrs
Instructor Bio:
Tariq Talat Nagah
- An AI Engineer, specializing in computer vision, real-time inference, and AI deployment, holding a B.Sc. in Computer and Information Science from Minya University
- He has delivered production-ready computer vision solutions across infrastructure, mobility, and analytics domains
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